Accurate prognostic prediction using molecular information is a challenging area of research, which is essential to develop precision medicine. In this paper, we develop translational models to identify major actionable proteins that are associated with clinical outcomes, like the survival time of patients. There are considerable statistical and computational challenges due to the large dimension of the problems. Furthermore, data are available for different tumor types; hence data integration for various tumors is desirable. Having censored survival outcomes escalates one more level of complexity in the inferential procedure. We develop Bayesian hierarchical survival models, which accommodate all the challenges mentioned here. We use the hierarchical Bayesian accelerated failure time model for survival regression. Furthermore, we assume sparse horseshoe prior distribution for the regression coefficients to identify the major proteomic drivers. We borrow strength across tumor groups by introducing a correlation structure among the prior distributions. The proposed methods have been used to analyze data from the recently curated “The Cancer Proteome Atlas” (TCPA), which contains reverse‐phase protein arrays–based high‐quality protein expression data as well as detailed clinical annotation, including survival times. Our simulation and the TCPA data analysis illustrate the efficacy of the proposed integrative model, which links different tumors with the correlated prior structures.

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Bayesian approaches for criterion based selection include the marginal likelihood based highest posterior model (HPM) and the deviance information criterion (DIC). The DIC is popular in practice as it can often be estimated from sampling‐based methods with relative ease and DIC is readily available in various Bayesian software. We find that sensitivity of DIC‐based selection can be high, in the range of 90–100%. However, correct selection by DIC can be in the range of 0–2%. These performances persist consistently with increase in sample size. We establish that both marginal likelihood and DIC asymptotically disfavour under‐fitted models, explaining the high sensitivities of both criteria. However, mis‐selection probability of DIC remains bounded below by a positive constant in linear models with g‐priors whereas mis‐selection probability by marginal likelihood converges to 0 under certain conditions. A consequence of our results is that not only the DIC cannot asymptotically differentiate between the data‐generating and an over‐fitted model, but, in fact, it cannot asymptotically differentiate between two over‐fitted models as well. We illustrate these results in multiple simulation studies and in a biomarker selection problem on cancer cachexia of non‐small cell lung cancer patients. We further study the performances of HPM and DIC in generalized linear model as practitioners often choose to use DIC that is readily available in software in such non‐conjugate settings.

Abstract Motivation It is well known that the integration among different data-sources is reliable because of its potential of unveiling new functionalities of the genomic expressions, which might be dormant in a single-source analysis. Moreover, different studies have justified the more powerful analyses of multi-platform data. Toward this, in this study, we consider the circadian genes’ omics profile, such as copy number changes and RNA-sequence data along with their survival response. We develop a Bayesian structural equation modeling coupled with linear regressions and log normal accelerated failure-time regression to integrate the information between these two platforms to predict the survival of the subjects. We place conjugate priors on the regression parameters and derive the Gibbs sampler using the conditional distributions of them. Results Our extensive simulation study shows that the integrative model provides a better fit to the data than its closest competitor. The analyses of glioblastoma cancer data and the breast cancer data from TCGA, the largest genomics and transcriptomics database, support our findings. Availability and implementation The developed method is wrapped in R package available at https://github.com/MAITYA02/semmcmc. Supplementary information Supplementary data are available at Bioinformatics online.

Ordinal data are often found in clinical studies. Sometimes the analysis of this data is done using binary logistic regression after collapsing the categories of ordinal responses. However, these analyses may not be appropriate in practice because either the assumptions are violated or because the information is lost. Cumulative logistic regression is shown to be a better alternative approach. The efficiency of cumulative logistic regression is demonstrated using simulation studies. A novel sequential testing approach is suggested in the context of cancer data. In addition, in the absence of the proper knowledge of the data, an automatic data-driven approach is also proposed. The efficacy of these proposals are illustrated via simulation studies. AMS 2000 subject classification code: 92D99